What factors lead to chronic respiratory disease? Researchers investigated this question using health data from about 780 infants. Their analysis, published in The Lancet Digital Health, shows that children’s risk of developing asthma later in life can be more reliably predicted by observing the dynamic development of symptoms during the first year of life.
Genetic predisposition, passive smoking, high levels of air pollution and infections are only a few of the risk factors for asthma. Each factor has only a small influence on its own. It is their interplay that makes asthma more likely, according to the hypothesis of an international research committee, of which Professor Urs Frey of the University of Basel and the University Children’s Hospital Basel is a member.
Together with Dr Uri Nahum from his team and international colleagues, Frey investigated how the interaction of these factors during the course of the first year of life affected children’s developing respiratory systems. The analysis was based on health data from two cohorts, amounting to around 780 healthy infants born in various European countries.
A new way of looking at chronic illness
For both cohorts the researchers calculated the network of interactions between a range of known risk factors for every week of each child’s life, and then compared these with the appearance of symptoms such as coughing or wheezing. “Observing this interaction of risk factors in the context of dynamic development over time is a new way of looking at chronic illnesses,” underlines Frey. It is a case of watching the developing lungs adapting to their environment.
And it was exactly this, the adaptation of the lungs, that differentiated the group of children who developed asthma at between two and six years of age from those who had not developed it by the time they started school (generally at six years old in Switzerland). “It’s a nice, practical example of the value of digital health data, which were first quantified mathematically using these kinds of dynamic network analyses,” says Frey.
The findings cannot yet be used for early diagnosis in individual children. However, according to Frey: “With greater amounts of data and machine learning, it would certainly be conceivable to calculate a risk profile for individual children in the future.” Nowadays, digital health data is relatively easy to collect with the help of smartphone apps.
Source: University of Basel
